From a53785369085f15a31e2bc295c671bd634a7679c Mon Sep 17 00:00:00 2001
From: Nicholas Clark <nicholas.j.clark1214@gmail.com>
Date: Mon, 18 Nov 2024 13:02:37 +1000
Subject: [PATCH] fix one bad test; update site

---
 R/globals.R                 |  3 ++-
 docs/index.html             |  8 +++----
 index.Rmd                   |  4 ++--
 index.md                    | 43 ++++++++++++++++++++++---------------
 tests/testthat/test-mvgam.R |  2 +-
 5 files changed, 35 insertions(+), 25 deletions(-)

diff --git a/R/globals.R b/R/globals.R
index 12c1cfa5..cac62eca 100644
--- a/R/globals.R
+++ b/R/globals.R
@@ -21,4 +21,5 @@ utils::globalVariables(c("y", "year", "smooth_vals", "smooth_num",
                          "horizon", "target", "Series", "evd", "mean_evd",
                          "total_evd", "smooth_label", "by_variable",
                          "gr", "tot_subgrs", "subgr", "lambda",
-                         "level", "sim_hilbert_gp", "trend_model"))
+                         "level", "sim_hilbert_gp", "trend_model",
+                         "jags_path"))
diff --git a/docs/index.html b/docs/index.html
index 973d6682..56eff4e7 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -5,14 +5,14 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<meta name="description" content="Fit Bayesian Dynamic Generalized Additive Models to sets of time series. Users can build dynamic nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software Stan. References: Clark &amp; Wells (2022) &lt;doi:10.1111/2041-210X.13974&gt;.">
+<meta name="description" content="Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software Stan. References: Clark &amp; Wells (2022) &lt;doi:10.1111/2041-210X.13974&gt;.">
 <title>Multivariate (Dynamic) Generalized Additive Models • mvgam</title>
 <script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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 <!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Multivariate (Dynamic) Generalized Additive Models">
-<meta property="og:description" content="Fit Bayesian Dynamic Generalized Additive Models to sets of time series. Users can build dynamic nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software Stan. References: Clark &amp; Wells (2022) &lt;doi:10.1111/2041-210X.13974&gt;.">
+<meta property="og:description" content="Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software Stan. References: Clark &amp; Wells (2022) &lt;doi:10.1111/2041-210X.13974&gt;.">
 <meta property="og:image" content="https://nicholasjclark.github.io/mvgam/logo.png">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
@@ -79,7 +79,7 @@
 <h2 id="mvgam">mvgam<a class="anchor" aria-label="anchor" href="#mvgam"></a>
 </h2>
 <p><strong>M</strong>ulti<strong>V</strong>ariate (Dynamic) <strong>G</strong>eneralized <strong>A</strong>ddivite <strong>M</strong>odels</p>
-<p>The goal of <code>mvgam</code> is to use a Bayesian framework to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components. The package provides an interface to fit Bayesian DGAMs using <code>Stan</code> as the backend. The formula syntax is based on that of the package <code>mgcv</code> to provide a familiar GAM modelling interface. The motivation for the package and some of its primary objectives are described in detail by <a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13974" class="external-link">Clark &amp; Wells 2022</a> (published in <em>Methods in Ecology and Evolution</em>). An introduction to the package and some worked examples are shown in this seminar: <a href="https://www.youtube.com/watch?v=0zZopLlomsQ" target="_blank" class="external-link">Ecological Forecasting with Dynamic Generalized Additive Models</a>.</p>
+<p>The goal of <code>mvgam</code> is to fit Bayesian (Dynamic) Generalized Additive Models. This package constructs State-Space models that can include highly flexible nonlinear predictor effects for both process and observation components by leveraging functionalities from the impressive <a href="https://paulbuerkner.com/brms/" target="_blank" class="external-link"><code>brms</code></a> and <a href="https://cran.r-project.org/web/packages/mgcv/index.html" target="_blank" class="external-link"><code>mgcv</code></a> packages. This allows <code>mvgam</code> to fit a wide range of models, including hierarchical ecological models such as N-mixture or Joint Species Distribution models, as well as univariate and multivariate time series models with imperfect detection. The original motivation for the package is described in <a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13974" target="_blank" class="external-link">Clark &amp; Wells 2022</a> (published in <em>Methods in Ecology and Evolution</em>), with additional inspiration on the use of Bayesian probabilistic modelling coming from <a href="https://betanalpha.github.io/writing/" target="_blank" class="external-link">Michael Betancourt</a>, <a href="https://www.bu.edu/earth/profiles/michael-dietze/" target="_blank" class="external-link">Michael Dietze</a> and <a href="https://www.durham.ac.uk/staff/sarah-e-heaps/" target="_blank" class="external-link">Sarah Heaps</a>, among many others.</p>
 </div>
 <div class="section level2">
 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
@@ -142,7 +142,7 @@ <h2 id="getting-started">Getting started<a class="anchor" aria-label="anchor" hr
 <code class="sourceCode R"><span><span class="fu"><a href="reference/plot_mvgam_series.html">plot_mvgam_series</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">data</span><span class="op">$</span><span class="va">data_train</span>, series <span class="op">=</span> <span class="st">'all'</span><span class="op">)</span></span></code></pre></div>
 <figure><img src="reference/figures/README-beta_sim-1.png" alt="Simulating and analysing multivariate time series with Dynamic Generalized Additive Models"><figcaption aria-hidden="true">
 Simulating and analysing multivariate time series with Dynamic Generalized Additive Models
-</figcaption></figure><p>Fit a DGAM to these series that uses a hierarchical cyclic seasonal smooth term to capture variation in seasonality among series. The model also includes series-specific latent Gaussian Processes with squared exponential covariance functions to capture temporal dynamics</p>
+</figcaption></figure><p>Fit a State-Space GAM to these series that uses a hierarchical cyclic seasonal smooth term to capture variation in seasonality among series. The model also includes series-specific latent Gaussian Processes with squared exponential covariance functions to capture temporal dynamics</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">mod</span> <span class="op">&lt;-</span> <span class="fu"><a href="reference/mvgam.html">mvgam</a></span><span class="op">(</span><span class="va">y</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">season</span>, bs <span class="op">=</span> <span class="st">'cc'</span>, k <span class="op">=</span> <span class="fl">7</span><span class="op">)</span> <span class="op">+</span></span>
 <span>               <span class="fu"><a href="https://rdrr.io/pkg/mgcv/man/s.html" class="external-link">s</a></span><span class="op">(</span><span class="va">season</span>, by <span class="op">=</span> <span class="va">series</span>, m <span class="op">=</span> <span class="fl">1</span>, k <span class="op">=</span> <span class="fl">5</span><span class="op">)</span>,</span>
diff --git a/index.Rmd b/index.Rmd
index 033a7120..0cb489fd 100644
--- a/index.Rmd
+++ b/index.Rmd
@@ -10,7 +10,7 @@ always_allow_html: true
 ## mvgam
 **M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
 
-The goal of `mvgam` is to use a Bayesian framework to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components. The package provides an interface to fit Bayesian DGAMs using `Stan` as the backend. The formula syntax is based on that of the package `mgcv` to provide a familiar GAM modelling interface. The motivation for the package and some of its primary objectives are described in detail by [Clark & Wells 2022](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13974) (published in *Methods in Ecology and Evolution*). An introduction to the package and some worked examples are shown in this seminar: [Ecological Forecasting with Dynamic Generalized Additive Models](https://www.youtube.com/watch?v=0zZopLlomsQ){target="_blank"}.
+The goal of `mvgam` is to fit Bayesian (Dynamic) Generalized Additive Models. This package constructs State-Space models that can include highly flexible nonlinear predictor effects for both process and observation components by leveraging functionalities from the impressive [`brms`](https://paulbuerkner.com/brms/){target="_blank"} and [`mgcv`](https://cran.r-project.org/web/packages/mgcv/index.html){target="_blank"} packages. This allows `mvgam` to fit a wide range of models, including hierarchical ecological models such as N-mixture or Joint Species Distribution models, as well as univariate and multivariate time series models with imperfect detection. The original motivation for the package is described in [Clark & Wells 2022](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13974){target="_blank"} (published in *Methods in Ecology and Evolution*), with additional inspiration on the use of Bayesian probabilistic modelling coming from [Michael Betancourt](https://betanalpha.github.io/writing/){target="_blank"}, [Michael Dietze](https://www.bu.edu/earth/profiles/michael-dietze/){target="_blank"} and [Sarah Heaps](https://www.durham.ac.uk/staff/sarah-e-heaps/){target="_blank"}, among many others.
   
 ## Installation
 Install the stable version from CRAN using: `install.packages('mvgam')`, or install the development version from `GitHub` using:
@@ -58,7 +58,7 @@ plot_mvgam_series(data = data$data_train, series = 'all')
 ```
 ![Simulating and analysing multivariate time series with Dynamic Generalized Additive Models](man/figures/README-beta_sim-1.png)
 
-Fit a DGAM to these series that uses a hierarchical cyclic seasonal smooth term to capture variation in seasonality among series. The model also includes series-specific latent Gaussian Processes with squared exponential covariance functions to capture temporal dynamics
+Fit a State-Space GAM to these series that uses a hierarchical cyclic seasonal smooth term to capture variation in seasonality among series. The model also includes series-specific latent Gaussian Processes with squared exponential covariance functions to capture temporal dynamics
 ```{r, eval = FALSE}
 mod <- mvgam(y ~ s(season, bs = 'cc', k = 7) +
                s(season, by = series, m = 1, k = 5),
diff --git a/index.md b/index.md
index 9da620ce..59b8ccd3 100644
--- a/index.md
+++ b/index.md
@@ -7,19 +7,28 @@
 
 **M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
 
-The goal of `mvgam` is to use a Bayesian framework to estimate
-parameters of Dynamic Generalized Additive Models (DGAMs) for time
-series with dynamic trend components. The package provides an interface
-to fit Bayesian DGAMs using `Stan` as the backend. The formula syntax is
-based on that of the package `mgcv` to provide a familiar GAM modelling
-interface. The motivation for the package and some of its primary
-objectives are described in detail by [Clark & Wells
-2022](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13974)
-(published in *Methods in Ecology and Evolution*). An introduction to
-the package and some worked examples are shown in this seminar:
-<a href="https://www.youtube.com/watch?v=0zZopLlomsQ"
-target="_blank">Ecological Forecasting with Dynamic Generalized Additive
-Models</a>.
+The goal of `mvgam` is to fit Bayesian (Dynamic) Generalized Additive
+Models. This package constructs State-Space models that can include
+highly flexible nonlinear predictor effects for both process and
+observation components by leveraging functionalities from the impressive
+<a href="https://paulbuerkner.com/brms/"
+target="_blank"><code>brms</code></a> and
+<a href="https://cran.r-project.org/web/packages/mgcv/index.html"
+target="_blank"><code>mgcv</code></a> packages. This allows `mvgam` to
+fit a wide range of models, including hierarchical ecological models
+such as N-mixture or Joint Species Distribution models, as well as
+univariate and multivariate time series models with imperfect detection.
+The original motivation for the package is described in <a
+href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13974"
+target="_blank">Clark &amp; Wells 2022</a> (published in *Methods in
+Ecology and Evolution*), with additional inspiration on the use of
+Bayesian probabilistic modelling coming from
+<a href="https://betanalpha.github.io/writing/" target="_blank">Michael
+Betancourt</a>,
+<a href="https://www.bu.edu/earth/profiles/michael-dietze/"
+target="_blank">Michael Dietze</a> and
+<a href="https://www.durham.ac.uk/staff/sarah-e-heaps/"
+target="_blank">Sarah Heaps</a>, among many others.
 
 ## Installation
 
@@ -103,10 +112,10 @@ alt="Simulating and analysing multivariate time series with Dynamic Generalized
 time series with Dynamic Generalized Additive Models</figcaption>
 </figure>
 
-Fit a DGAM to these series that uses a hierarchical cyclic seasonal
-smooth term to capture variation in seasonality among series. The model
-also includes series-specific latent Gaussian Processes with squared
-exponential covariance functions to capture temporal dynamics
+Fit a State-Space GAM to these series that uses a hierarchical cyclic
+seasonal smooth term to capture variation in seasonality among series.
+The model also includes series-specific latent Gaussian Processes with
+squared exponential covariance functions to capture temporal dynamics
 
 ``` r
 mod <- mvgam(y ~ s(season, bs = 'cc', k = 7) +
diff --git a/tests/testthat/test-mvgam.R b/tests/testthat/test-mvgam.R
index 4c752a77..b2eb6387 100644
--- a/tests/testthat/test-mvgam.R
+++ b/tests/testthat/test-mvgam.R
@@ -209,7 +209,7 @@ test_that("JAGS setups should work", {
   expect_true(inherits(get_mvgam_priors(y ~ s(season),
                                         trend_model = 'RW',
                                         data = simdat$data_train,
-                                        family = Gamma(),
+                                        family = gaussian(),
                                         use_stan = FALSE),
                        'data.frame'))
   mod <- mvgam(y ~ s(season),